Open AccessThe relationship between active learning, course innovation, and teaching Earth systems thinking: A structural equation modeling approach
Author(s) -
Nicholas A. Soltis,
Karen S. McNeal,
Cory Forbes,
Diane Lally
Publication year - 2019
Publication title -
geosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.879
H-Index - 58
ISSN - 1553-040X
DOI - 10.1130/ges02071.1
Subject(s) - structural equation modeling , active learning (machine learning) , mathematics education , teaching method , work (physics) , confirmatory factor analysis , exploratory research , psychology , computer science , earth science , pedagogy , engineering , sociology , geology , artificial intelligence , social science , mechanical engineering , machine learning
Earth systems thinking (EST), or thinking of the Earth as a complex system made up of interworking subsystems, has been shown to reflect the highest level of knowing and understanding in the geosciences. Previous work has found four frameworks of EST that repeatedly appear in the geoscience education literature. This study aims to quantitatively build on this work by employing structural equation modeling to understand the current state of EST teaching as shown by the 2016 iteration of the National Geoscience Faculty Survey (United States; n = 2615). Exploratory and confirmatory factor analyses were conducted on survey items to understand and develop three models, one for EST teaching practices, one for course changes, and one for active-learning teaching practices. Analyses revealed that reported EST teaching practices relate back to the four EST frameworks proposed in the literature. The three models explored in this study were used to build a full structural model, where it was hypothesized that active-learning teaching practices would predict EST course changes and EST teaching. However, the model revealed that EST course changes mediate, or bring about, the relationship between active-learning teaching practices and EST teaching. In other words, the relationship between active-learning and EST teaching practices is not direct. This implies the need for continued efforts to provide professional development opportunities in both active-learning teaching practices and EST, as active-learning practices are not sufficient to implicitly teach EST skills. Results also revealed that the teaching approaches that emphasize modeling and complexity sciences had the weakest relationship to the broader EST teaching practices, suggesting a need for more professional development opportunities as they relate to systems modeling, quantitative reasoning, and complexity sciences in the context of the Earth sciences.